Readmission risk assessment based on chronobiological rhythms

ABSTRACT

Systems and methods for monitoring patients with a chronic disease are described. A patient management system may sense physiological signals from a patient using one or more implantable or other ambulatory sensors, and generate from the physiological signals a chronobiological rhythm indicator (CRI) such as indicating a circadian rhythm. A reference CRI associated with a prior hospital admission event of the patient may be provided to the patient management system, which compares the CRI to the reference CRI and generates a readmission risk score indicating the patient&#39;s risk of subsequent hospital readmission due to a worsened condition of the chronic disease. The readmission risk score may be provided to a user or a process, or used to initiate or adjust a therapy delivered to the patient.

CLAIM OF PRIORITY

This application is a continuation of U.S. application Ser. No.16/653,011, filed Oct. 15, 2019, which is a continuation of U.S.application Ser. No. 15/633,278, filed Jun. 26, 2017, which claims thebenefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional PatentApplication Ser. No. 62/358,974, filed on Jul. 6, 2016, each of which isherein incorporated by reference in its entirety.

TECHNICAL FIELD

This document relates generally to medical devices, and moreparticularly, to systems and methods for monitoring patients havingmedical device.

BACKGROUND

Hospital readmission, especially for people with chronic diseases, is amajor contributor to high healthcare costs and has a huge economicimpact on the healthcare system. Nearly 20 percent of Medicare patientsdischarged from hospitals are readmitted within 30 days for anexacerbation of the diagnosed condition.

Congestive heart failure (CHF or HF) is a chronic cardiac disease and aleading cause of death in the United States. CHF occurs when the heartis unable to adequately supply enough blood to maintain a healthyphysiological state. CHF may be treated by drug therapy, or by animplantable medical device (IMD) such as for providing cardiacelectrostimulation therapies, including resynchronization therapy (CRT)to correct cardiac dyssynchrony within a ventricle or betweenventricles.

Patients with worsened HF, such as decompensated heart failure, may havea high hospital readmission rate within six months following hospitaldischarge. Readmission is responsible for high cost of heart failuremanagement. An unplanned readmission occurs when a patient is readmittedto a hospital within a certain period of time (e.g., 30 days) afterhaving been discharged from the hospital for treatment of the same orrelated condition, such as heart failure, pneumonia, or othercomorbidities.

Chronic disease management can reduce hospital readmissions and lowerhealth care costs. For example, reduction of unplanned readmissions forHF or HF comorbidities may be achieved through reliably identifying thepatients with worsened HF condition. Proper post-discharge patientmonitoring may lead to reliable and robust readmission decisions,thereby reducing the readmission rate and providing timely treatment tothose who require rehospitalization.

SUMMARY

This document discusses, among other things, a patient management systemfor monitoring patients with a chronic disease, such as CHF. The patientmanagement system may sense physiological signals from a patient usingone or more implantable or other ambulatory sensors, and generate fromthe physiological signals a chronobiological rhythm indicator (CRI) suchas a circadian rhythm indicator. A reference CRI associated with a priorhospital admission event of the patient may be provided to the patientmanagement system that compares the CRI to the reference CRI andgenerate a readmission risk score indicating the patient's risk ofsubsequent hospital readmission for a worsened condition of the chronicdisease. The readmission risk score may be provided to a user or aprocess. The readmission risk score may be provided to a user or aprocess, or used to initiate or adjust a therapy delivered to thepatient.

Example 1 is a system for managing a patient with a chronic disease. Thesystem may comprise: a sensor circuit including sense amplifier circuitsto sense one or more physiological signals; a signal processor circuitconfigured to generate a chronobiological rhythm indicator (CRI) fromthe one or more physiological signals; and a detector circuit coupled tothe signal processor circuit and configured to determine a readmissionrisk score using the generated CRI and a reference CRI associated with aprior hospital admission event of the patient, the readmission riskscore indicating a degree of risk of subsequent hospital readmission fora worsened condition of the chronic disease.

In Example 2, the subject matter of Example 1 may optionally include atherapy circuit adapted to initiate or adjust a therapy delivered to thepatient in response to the readmission risk score satisfying acondition.

In Example 3, the subject matter of any one or more of Examples 1-2 mayoptionally include the detector circuit for detecting the chronicdisease including a heart failure, and the signal processor circuit isconfigured to generate the CRI from the one or more physiologicalsignals including: a heart sound signal; an endocardial accelerationsignal; a heart rate signal; a thoracic impedance signal; a respirationsignal; a pressure signal; a chemical signal; an activity intensitysignal; or a posture signal.

In Example 4, the subject matter of any one or more of Examples 1-3 mayoptionally include a sensor selector circuit configured to select atleast one physiological signal from the two or more physiologicalsignals according to the reference CRI associated with the priorhospital admission event. The detector circuit may be configured todetermine the readmission risk score using the generated CRI generatedfrom the selected at least one physiological signal.

In Example 5, the subject matter of Example 4 may optionally include thesensor selector circuit configured to select the at least onephysiological signal according to a change from a pre-admission CRI to apost-discharge CRI associated with the prior hospital admission event.

In Example 6, the subject matter of any one or more of Examples 1-5 mayoptionally include the signal processor circuit that is configured togenerate the reference CRI from one or more physiological signals sensedduring a post-discharge period following the prior hospital admissionevent; and the detector circuit is configured to determine thereadmission risk score in response to a relative difference between thegenerated CRI and the reference CRI falling below a threshold.

In Example 7, the subject matter of any one or more of Examples 1-6 mayoptionally include the signal processor circuit that is configured togenerate the reference CRI from one or more physiological signals sensedduring a pre-admission period preceding the prior hospital admissionevent. The detector circuit may be configured to determine thereadmission risk score in response to a relative difference between thegenerated CRI and the reference CRI exceeding a threshold.

In Example 8, the subject matter of any one or more of Examples 1-7 mayoptionally include the processor circuit that is configured to generatea first CRI from a first physiological signal and a second CRI from adifferent second physiological signal. The detector circuit may beconfigured to generate a first reference CRI from the firstphysiological signal during a first time period associated with theprior hospital admission event, and a second reference CRI from thesecond physiological signal during a second time period associated withthe prior hospital admission event, and determine the readmission riskscore using both a comparison between the first CRI and the firstreference CRI and a comparison between the second CRI and the secondreference CRI.

In Example 9, the subject matter of any one or more of Examples 1-8 mayoptionally include the detector circuit that is configured to determinethe readmission risk score further using time elapsed from the priorhospital admission event.

In Example 10, the subject matter of any one or more of Examples 1-9 mayoptionally include the sensor circuit that is configured to sense atleast two physiological signals. The signal processor circuit mayinclude an ellipticity analyzer circuit configured to form amultidimensional data representation of the at least two physiologicalsignals in a multidimensional signal space, and determine an ellipticityattribute from the multidimensional data representation; and the signalprocessor circuit is configured to generate the CRI using theellipticity attribute.

In Example 11, the subject matter of Example 10 may optionally includethe signal processor circuit that is configured to generate thereference CRI including a reference ellipticity attribute from amultidimensional data representation of at least two physiologicalsignals during respective time periods associated with the priorhospital admission event. The detector circuit may be configured todetermine the readmission risk score including a relative change of theellipticity attribute from the reference ellipticity attribute.

In Example 12, the subject matter of any one or more of Examples 10-11may optionally include the ellipticity analyzer circuit that isconfigured to determine the ellipticity attribute including acovariation pattern of the at least two physiological signals in themultidimensional signal space.

In Example 13, the subject matter of any one or more of Examples 10-12may optionally include the ellipticity analyzer circuit that isconfigured to: compute a covariance matrix using the at least twophysiological signals; determine, from the covariance matrix, two ormore principal components in the multidimensional signal space or aplurality of eigenvalues associated with the two or more principalcomponents; and determine the ellipticity attribute using the two ormore principal components, a projection of the multidimensional datarepresentation along at least one of the two or more determinedprincipal components, or a relative measure among the plurality ofeigenvalues.

In Example 14, the subject matter of Example 13 may optionally includethe ellipticity analyzer circuit that is configured to: compute areference covariance matrix using the at least two physiological signalsduring respective time periods associated with the prior hospitaladmission event; and determine two or more reference principalcomponents from the reference covariance matrix; determine a firstprojection of the multidimensional data representation along at leastone of the reference principal components, and a second projection of amultidimensional data representation of the at least two physiologicalsignals associated with the prior hospital admission event along the atleast one of the reference principal components; and generate the CRIfrom first projection and generate the reference CRI from the secondprojection; and wherein the detector circuit is configured to determinethe readmission risk score including a relative change of the CRI fromthe reference CRI.

In Example 15, the subject matter of any one or more of Examples 1-14may optionally include a trending circuit configured to generate a trendof CRI over time. The detector circuit may be configured to determinethe readmission risk score further using the trend of CRI. Thereadmission risk score indicates a low readmission risk corresponding toan increasing trend of CRI, or indicates a high readmission riskcorresponding to a decreasing trend of CRI.

Example 16 is a method for managing a patient with a chronic disease.The method comprises steps of: sensing one or more physiologicalsignals; generating a chronobiological rhythm indicator (CRI) from theone or more physiological signals; and receiving a reference CRIassociated with a prior hospital admission event of the patient; anddetermining a readmission risk score using the generated CRI and thereference CRI, the readmission risk score indicating a degree of risk ofsubsequent hospital readmission for a worsened condition of the chronicdisease; and providing the readmission risk score to a user or aprocess.

In Example 17, the subject matter of Example 12 may optionally includeadjusting or initiating a therapy, via a therapy delivery circuit, fordelivery to the patient in response to the readmission risk scoresatisfying a condition.

In Example 18, the subject matter of any one or more of Examples 16-17may optionally include selecting at least one physiological signal fromthe two or more physiological signals according to the reference CRIassociated with the prior hospital admission event. The readmission riskscore is determined using the CRI generated from the selected at leastone physiological signal.

In Example 19, the subject matter of any one or more of Examples 16-18may optionally include generating one or more reference CRIs from one ormore physiological signals sensed during time periods including apost-discharge period following the prior hospital admission event or apre-admission period preceding the prior hospital admission event. Thereadmission risk score may be determined using a comparison between thegenerated CRI and the one or more reference CRIs during thepost-discharge period or the pre-admission period.

In Example 20, the subject matter of any one or more of Examples 16-19may optionally include generating the CRI including generating a firstCRI from a first physiological signal and a second CRI from a differentsecond physiological signal. The step of receiving the reference CRI mayinclude generating a first reference CRI from the first physiologicalsignal during a first time period associated with the prior hospitaladmission event, and a second reference CRI from the secondphysiological signal during a second time period associated with theprior hospital admission event; and determining the readmission riskscore includes a combination of both a comparison between the first CRIand the first reference CRI and a comparison between the second CRI andthe second reference CRI.

In Example 21, the subject matter of any one or more of Examples 16-20may optionally include generating the CRI that includes generating amultidimensional data representation of at least two physiologicalsignals in a multidimensional signal space, and determining anellipticity attribute from the multidimensional data representation. Thestep of receiving the reference CRI may include generating a referenceellipticity attribute from a multidimensional data representation of atleast two physiological signals during respective time periodsassociated with the prior hospital admission event. The step ofdetermining the readmission risk score may include computing a relativechange of the ellipticity attribute from the reference ellipticityattribute.

In Example 22, the subject matter of Example 21 may optionally includethe ellipticity attribute that includes a covariation pattern of theintensities of the at least two physiological signals in themultidimensional signal space.

In Example 23, the subject matter of any one or more of Examples 21-22may optionally include determining the ellipticity attribute, which mayinclude: computing a covariance matrix using the at least twophysiological signals; determining, from the covariance matrix, two ormore principal components or a plurality of eigenvalues associated withthe two or more principal components; and determining the ellipticityattribute using the two or more principal components, a projection ofthe multidimensional data representation along at least one of the twoor more determined principal components, or a relative measure among theplurality of eigenvalues.

The systems, devices, and methods discussed in this document may improvethe medical technology of automated monitoring of patients with chronicdisease, such as heart failure. The readmission risk analysis based onchronobiological rhythm indicator (CRI) as discussed in this documentmay enhance the performance and functionality of a medical system or anambulatory medical device for detecting a chronic disease. In certainexamples, the enhanced device functionality may include more timely andaccurate detection of worsened HF condition at little to no additionalcost, thereby reducing unplanned readmissions for HF or HFcomorbidities. The improvement in system performance and functionalitycan provide reliable and robust readmission decisions, reduce thereadmission rate and thus the healthcare costs associated withhospitalization, and provide timely treatment to those who requirerehospitalization. The systems, devices, and methods discussed in thisdocument also allow for more efficient device memory usage, such as bystoring CRI that are clinically more relevant to readmission risk. Asfewer hospital readmissions are resulted, device battery life can beextended, fewer unnecessary drugs and procedures may be scheduled,prescribed, or provided, and an overall system cost savings may berealized.

Although much of the discussion herein focuses on readmission ofpatients with worsened HF, this is meant only by way of example but notlimitation. Systems and methods discussed in this document may also besuitable for monitoring patients who may be at risk of hospitalreadmission for various sorts of chronic diseases including, forexample, coronary artery disease, chronic obstructive pulmonary disease,chronic kidney disease, among many others.

This Summary is an overview of some of the teachings of the presentapplication and not intended to be an exclusive or exhaustive treatmentof the present subject matter. Further details about the present subjectmatter are found in the detailed description and appended claims. Otheraspects of the invention will be apparent to persons skilled in the artupon reading and understanding the following detailed description andviewing the drawings that form a part thereof, each of which are not tobe taken in a limiting sense. The scope of the present invention isdefined by the appended claims and their legal equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are illustrated by way of example in the figures ofthe accompanying drawings. Such embodiments are demonstrative and notintended to be exhaustive or exclusive embodiments of the presentsubject matter.

FIG. 1 illustrates generally an example of a patient management systemand portions of an environment in which the patient management systemmay operate.

FIG. 2 illustrates generally an example of a chronic disease monitoringsystem for monitoring a chronic disease in a patient and determining thepatient's risk of hospital readmission for the chronic disease.

FIG. 3 illustrates generally an example of a portion of a system forgenerating a reference CRI from a patient's physiological signalassociated with a prior medical event.

FIG. 4 illustrates generally an example of a multi-sensor readmissionrisk assessment system.

FIGS. 5A-B illustrate generally examples of an ellipticity analyzercircuit for determining a chronobiological rhythm indicator using aplurality of physiological signals or signal metric trends.

FIG. 6 illustrates generally an example of a method for monitoring achronic disease in a patient to determine a risk of hospitalreadmission.

FIG. 7 illustrates generally an example of a method for determining achronobiological rhythm indicator based on ellipticity analysis of aplurality of physiological signals or signal metric trends.

DETAILED DESCRIPTION

Disclosed herein are systems, devices, and methods for monitoringpatients with a chronic disease and assessing a risk of hospitalreadmission. A system may sense physiological signals from a patientusing one or more implantable or other ambulatory sensors, and generatefrom the physiological signals a chronobiological rhythm indicator (CRI)such as indicating a degree of circadian rhythm. The patient managementsystem may compare the CRI to a reference CRI associated with a priorhospital admission event of the patient, and generate a readmission riskscore indicating a risk of subsequent hospital readmission due to aworsened condition of the chronic disease. The readmission risk scoremay be provided to a user or a process, or used to initiate or adjust atherapy delivered to the patient.

FIG. 1 illustrates generally an example of a patient management system100 and portions of an environment in which the patient managementsystem 100 may operate. The patient management system 100 may include anambulatory system 105 associated with a patient body 102, an externalsystem 125, and a telemetry link 115 providing for communication betweenthe ambulatory system 105 and the external system 125.

The ambulatory system 105 may include an ambulatory medical device (AMD)110 and a therapy delivery system such as a lead system 108. The AMD 110may include an implantable device that may be implanted within the body102 and coupled to a heart 101 via the lead system 108. Examples of theimplantable device may include, but are not limited to, pacemakers,pacemaker/defibrillators, cardiac resynchronization therapy (CRT)devices, cardiac remodeling control therapy (RCT) devices,neuromodulators, drug delivery devices, biological therapy devices,diagnostic devices such as cardiac monitors or loop recorders, orpatient monitors, among others. The AMD 110 may alternatively oradditionally include subcutaneously implanted devices such as asubcutaneous ICD or a subcutaneous diagnostic device, wearable medicaldevices, or other external monitoring or therapeutic medical devicessuch as a bedside monitor.

The lead system 108 may include one or more transvenously,subcutaneously, or non-invasively placed leads or catheters. Each leador catheter may include one or more electrodes. The arrangements anduses of the lead system 108 and the associated electrodes may bedetermined based on the patient need and the capability of the AMD 110.The lead system 108 and the associated electrodes may deliver therapy totreat cardiac or pulmonary diseases. The therapies may include pacing,cardioversion, defibrillation, neuromodulation, drug therapies, orbiological therapies, among other types of therapies. In an example, theelectrodes on the lead system 108 may be positioned inside or on asurface of at least a portion of the heart, such as a right atrium (RA),a right ventricle (RV), a left atrium (LA), a left ventricle (LV), orany tissue between or near the heart portions. In an example, the leadsystem 108 and the associated electrodes may be implanted subcutaneouslyor wearable on the patient body. The associated electrodes on the leadsystem 108 may be positioned at the patient's thorax or abdomen to senseintrinsic physiological signals indicative of cardiac or pulmonaryactivities, or physiological responses to diagnostic or therapeuticstimulations to a target tissue.

The AMD 110 may house an electronic circuit for sensing a physiologicalsignal, such as by using a physiological sensor or the electrodesassociated with the lead system 108. Examples of the physiologicalsignal may include one or more of electrocardiogram, intracardiacelectrogram, arrhythmia, heart rate, heart rate variability,intrathoracic impedance, intracardiac impedance, arterial pressure,pulmonary artery pressure, left atrial pressure, RV pressure, LVcoronary pressure, coronary blood temperature, blood oxygen saturation,blood chemistry such as electrolytes level, glucose level, creatininelevel, blood pH level, one or more heart sounds, intracardiacacceleration, physical activity or exertion level, physiologicalresponse to activity, posture, respiration rate, tidal volume,respiratory sounds, body weight, or body temperature. The AMD 110 mayinitiate or adjust therapies based on the sensed physiological signals.

The AMD 100 may include a readmission risk analyzer module 160 that maydetect a chronobiological rhythm from diagnostic data acquired by theambulatory system 105. The chronobiological rhythm may include acircadian rhythm representing a daily oscillation of physical, mentaland behavioral activities following an approximate 24-hour cycle.Alternatively or additionally, the chronobiological rhythm may includeweekly, monthly, seasonal, or other periodic changes in physiologicalactivities. An absence or presence, a pattern, or a change or rate ofchange from a reference chronobiological rhythm such as associated withan event in the patient medical history, may provide information ofprogression of a chronic disease or condition such as heart failure,chronic pulmonary disease, or chronic kidney disease, among others. Thereadmission risk analyzer module 160 may generate, based at least on thedetected chronobiological rhythm, a readmission risk indicator thatindicates a degree of risk of subsequent hospital readmission for aworsened chronic disease. The readmission risk analyzer module 160 maybe substantially included in the AMD 110. Alternatively, the readmissionrisk analyzer module 160 may be substantially included in the externalsystem 125, or be distributed between the ambulatory system 105 and theexternal system 125.

The external system 125 may be used to program the AMD 110. The externalsystem 125 may include a programmer, or a patient management system thatmay access the ambulatory system 105 from a remote location and monitorpatient status and/or adjust therapies. By way of non-limiting example,the external system 125 may include an external device 120 in proximityof the AMD 110, a remote device 124 in a location relatively distantfrom the AMD 110, and a telecommunication network 122 linking theexternal device 120 and the remote device 124. The telemetry link 115may be an inductive telemetry link, a capacitive telemetry link, or aradio-frequency (RF) telemetry link. The telemetry link 115 may providefor data transmission from the AMD 110 to the external system 125. Thismay include, for example, transmitting real-time physiological dataacquired by the AMD 110, extracting physiological data acquired by andstored in the AMD 110, extracting patient history data such as dataindicative of occurrences of arrhythmias, occurrences of decompensation,and therapy deliveries recorded in the AMD 110, and extracting dataindicating an operational status of the AMD 110 (e.g., battery statusand lead impedance). The telemetry link 115 may also provide for datatransmission from the external system 125 to the AMD 110. This mayinclude, for example, programming the AMD 110 to perform one or more ofacquiring physiological data, performing at least one self-diagnostictest (such as for a device operational status), analyzing thephysiological data to generate respiratory diagnostics such as presenceor worsening of a target respiratory condition, or delivering at leastone therapy to treat a respiratory disease.

Portions of the AMD 110 or the external system 125 may be implementedusing hardware, software, or any combination of hardware and software.Portions of the AMD 110 or the external system 125 may be implementedusing an application-specific circuit that may be constructed orconfigured to perform one or more particular functions, or may beimplemented using a general-purpose circuit that may be programmed orotherwise configured to perform one or more particular functions. Such ageneral-purpose circuit may include a microprocessor or a portionthereof, a microcontroller or a portion thereof, or a programmable logiccircuit, or a portion thereof. For example, a “comparator” may include,among other things, an electronic circuit comparator that may beconstructed to perform the specific function of a comparison between twosignals or the comparator may be implemented as a portion of ageneral-purpose circuit that may be driven by a code instructing aportion of the general-purpose circuit to perform a comparison betweenthe two signals.

FIG. 2 illustrates generally an example of a chronic disease monitoringsystem 200 for monitoring a chronic disease in a patient, such as achronic heart disease (e.g., heart failure decompensation, or coronaryartery disease), a chronic pulmonary disease (e.g., asthma,bronchoconstriction, COPD, pulmonary fibrosis, pneumoconiosis), or achronic kidney disease, among others. The chronic disease monitoringsystem 200 may be configured to monitor a progression of the chronicdisease following patient's prior discharge from the hospital, andassess a readmission risk such as due to worsening of the chronicdisease.

The chronic disease monitoring system 200 may include one or more of asensor circuit 210, a signal processor circuit 220, a detector circuit230, a controller circuit 240, and a user interface 250. The chronicdisease monitoring system 200 may include a reference chronobiologicalrhythm indicator (CRI) generator 260 for generating the reference CRIfor use in determining a readmission risk. In some examples, the chronicdisease monitoring system 200 may additionally include a therapy circuit270 configured to deliver therapy to the patient to treat or to preventfurther worsening of the chronic disease. At least a portion of thechronic disease monitoring system 200 may be implemented within the AMD110, distributed between two or more implantable or wearable medicaldevices, or distributed between the AMD 110 and the external system 125.

The sensor circuit 210 may include one or more sense amplifier circuitsto sense one or more physiologic signals indicative of spontaneousphysiologic activities or evoked physiologic activities when a part ofthe patient body (such as the heart or a nerve tissue) is stimulated.The physiological sensor circuit 210 may be coupled to one or moreelectrodes such as on the lead system 108, or one or more implantable,wearable, holdable, or other ambulatory sensors, to sense thephysiological signal(s). Examples of physiological sensors may includepressure sensors, flow sensors, impedance sensors, accelerometers,microphone sensors, respiration sensors, temperature sensors, orchemical sensors, among others. Examples of the physiological signalssensed by the physiological sensor circuit 210 may includeelectrocardiograph (ECG), an electrogram (EGM), an intrathoracicimpedance signal, an intracardiac impedance signal, an arterial pressuresignal, a pulmonary artery pressure signal, a RV pressure signal, a LVcoronary pressure signal, a coronary blood temperature signal, a bloodoxygen saturation signal, blood chemistry signals such as bloodelectrolytes level signal, glucose level signal or creatinine levelsignal, central venous pH value, a heart sound (HS) signal, anendocardial acceleration signal, an angular momentum sensor, a posturesignal, a physical activity signal, or a respiration signal, amongothers. The physiological sensor circuit 210 may additionally oralternatively be coupled to a storage device that stores the physiologicinformation, such as an external programmer, an electronic medicalrecord (EMR) system, or a memory unit, among other data storage devices.

The sensor circuit 210 may process the one or more physiologicalsignals, including, for example, amplification, digitization, filtering,or other signal conditioning operations, and generate one or more signalmetrics from the processed physiological signals. The one or more signalmetrics may be trended over time to produce respective signal metrictrends. In an example, the physiological sensor circuit 210 may receivea thoracic or cardiac impedance signal from the electrodes on the leadsystem 108, and generate a signal metric of impedance magnitude within aspecified frequency range. In another example, the physiological sensorcircuit 210 may sense a HS signal from an accelerometer, a microphone,or an acoustic sensor coupled to the AMD 110, and generate a HS metric.Examples of the HS metrics may include intensities of S1, S2, S3, or S4heart sounds, or timing of the S1, S2, S3, or S4 heart sound withrespect to a fiducial point such as a P wave, Q wave, or R wave in anECG. In another example, the physiological sensor circuit 210 may becoupled to a respiratory sensor including one of an accelerometer, amicrophone, an impedance sensor, or a flow sensor. Examples ofrespiration metrics may include one or more of a tidal volume, arespiration rate, a minute ventilation, a respiratory sound, or arapid-shallow breathing index (RSBI) computed as a ratio of arespiratory rate measurement to a tidal volume measurement. In anexample, the physiological sensor circuit 210 may receive multiplephysiological signals from multiple sensors. For example, thephysiological sensor circuit 210 may receive a pressure signal from apressure sensor and generate two or more cardiovascular blood pressuresignal metrics which may include systolic blood pressure, diastolicblood pressure, mean arterial pressure, and the timing metrics of thesepressure measurements with respect to a fiducial point. The pressuresensor may alternatively or additionally sense thoracic pressure orabdominal pressure.

The signal processor circuit 220 may include a sensor selector circuit222 and a chronobiological rhythm indicator (CRI) generator 224. Thesensor selector circuit 222 may select at least one physiological signalor signal metric for use by the CRI generator 224 to generate the CRI.The selection may be based on the physiological signal or signalmetric's sensitivity to the patient's chronobiological rhythms, such asan oscillatory pattern shown in a physiological signal or a signalmetric trend. As illustrated in FIG. 2 , the sensor selector circuit 222may be coupled to a reference CRI generator 260 that may generate areference indication of chronobiological rhythm associated with thepatient's prior hospital admission event, or other events in thepatient's medical history. The sensor selector circuit 222 may select atleast one physiological signal or signal metric based on the referenceindication of chronobiological rhythm. Compared to a signal or signalmetric trend that manifests no or a weak chronological oscillatorypattern, a signal or signal metric trend that manifests a strong daily,weekly, monthly, or seasonal oscillatory pattern is more sensitive tothe patient's chronobiological rhythm, and therefore may be selected bythe sensor selector circuit 222 for generating the CRI. The strength ofthe oscillatory pattern may be determined using an intensity differenceof the physiological signal or signal metric trend during an oscillationperiod, such as a maximum-to-minimum signal intensity difference duringapproximately 24-hour period for measuring a degree of circadian rhythm,or during other specified oscillation period. In some examples,information about physiological signals or signal metrics' sensitivitiesto chronobiological rhythms, including reference CRI or oscillatorypatterns associated with prior hospital admission or other medicalevents, may be stored in a memory circuit included within the chronicdisease monitoring system 200 or in a separate storage device such asthe EMR system. The sensor selector circuit 222 may be communicativelycoupled to the memory circuit, and select the physiological signals orsignal metrics based on the oscillatory patterns of the physiologicalsignals or signal metrics. Examples of the reference CRI generator arediscussed below, such as with reference to FIG. 3 .

The CRI generator 224 may generate a chronobiological rhythm indicator(CRI) from the selected physiological signals or signal metric trends.The CRI may include a statistical measure of daily, weekly, monthly, orother periodic maximum-to-minimum intensity (denoted by X_(pp))difference of a physiological signal or signal metric trend X over aspecified time period. In an example, the CRI includes a circadianrhythm indicator computed as a statistical measure of daily X_(pp)within one day. In another example, the CRI may be computed as astatistical measure of X_(pp) over a period of approximately 5-10 days.Examples of the statistical measure may include a first-order statisticsuch as mean, median, mode, or other central tendency measure, or asecond-order statistic such as variance, standard deviation, range,inter-quartile range, or other variability or spreadness measure. Thevariability of the circadian rhythm may indicate a regularity of thecircadian rhythm. For example, a larger variability indicates a lessregular circadian rhythm, and a smaller variability indicates a moreconsistent and regular circadian rhythm. Additionally or alternatively,the CRI generator 224 may perform a spectral analysis of the selectedphysiological signals or signal metric trends, and generate a spectralpeak of the chronobiological rhythm. The CRI may include one or morespectral parameters, such as a power, a center frequency, or a bandwidthof the spectral peak. In some examples, the CRI may be determined usingan ellipticity attribute represented in a multidimensional signal spacespanned by two or more selected physiological signals or signal metrics.Examples of the CRI based on ellipticity analysis are discussed below,such as with reference to FIGS. 5A and 5B.

Similar to the CRI generator 224, the reference CRI generator 260 maygenerate, for the candidate physiological signals or signal metrictrends, respective reference CRI (denoted by “rCRI”) over a specifiedtime period such as associated with prior hospital admission. In anexample, the rCRI may include reference statistical measure of dailymaximum-to-minimum signal intensity difference (denoted by “rX_(pp)”)over a specified time period, such as approximately 5-10 days. Inanother example, the rCRI may include reference spectral parametersderived from a spectral peak of the physiological signals or signalmetric trends over a specified time period such as associated with priorhospital admission. In yet another example, the rCRI may includereference ellipticity attribute represented in a multidimensional signalspace spanned by two or more selected physiological signals or signalmetrics. In some examples, the rCRI may be predetermined and stored in amemory or other storage device included within or otherwisecommunicatively coupled to the chronic disease monitoring system 200.

The detector circuit 230 may include a readmission risk generator 232coupled to the signal processor circuit 220 and the reference CRIgenerator 260. The readmission risk generator 232 may use the CRI andthe rCRI corresponding to the selected physiological signals or signalmetrics to generate a readmission risk score that indicates a degree ofrisk of subsequent hospital readmission due to worsening of the chronicdisease. For example, if CRI generator 224 generates the CRI from theselected thoracic impedance signal, then the rCRI corresponding to thethoracic impedance signal associated with a prior hospital admission maybe used in readmission risk generation. The readmission risk score maybe determined as a difference, ratio, or other relative measure betweenthe CRI and the rCRI. In an example, the readmission risk score iscomputed as a difference between the statistical measures of X_(pp) andthe statistical measure of rX_(pp), or between the spectral parameter(such as the center frequency or bandwidth of the spectral peak) and thereference spectral parameter. In another example, the readmission riskscore may be determined as a similarity measure between an ellipticityattribute and a reference ellipticity attribute, which is discussedbelow with reference to FIGS. 5A and 5B. The readmission risk score maytake continuous values. Alternatively, the readmission risk score may becompared to one or more threshold values or ranges, and categorized intodiscrete categorical levels such as high, medium, or low risk ofreadmission. In some examples, the readmission risk generator 232 maygenerate a composite risk score using multiple physiological signals orsignal metric trends, such as discussed below with reference to FIG. 4 .

In an example, the detector circuit 232 may include a trending circuitfor generating a trend of CRI over time. The readmission risk generator232 may determine the readmission risk score further using the CRItrend. In an example, the readmission risk score may be inverselyproportional to the CRI trend, such that an increasing trend of CRI maycorrespond to a low readmission risk score, and a decreasing trend ofCRI may correspond to a high readmission risk.

One or more of the signal processor circuit 220 or the detector circuit230 may be implemented as a part of a microprocessor circuit. Themicroprocessor circuit may be a dedicated processor such as a digitalsignal processor, application specific integrated circuit (ASIC),microprocessor, or other type of processor for processing informationincluding the physiological signals received from the sensor circuit210. Alternatively, the microprocessor circuit may be a general purposeprocessor that may receive and execute a set of instructions ofperforming the functions, methods, or techniques described herein.

The signal processor circuit 220 or the detector circuit 230 may includecircuit sets comprising one or more other circuits or sub-circuits.These circuits may, alone or in combination, perform the functions,methods, or techniques described herein. In an example, hardware of thecircuit set may be immutably designed to carry out a specific operation(e.g., hardwired). In an example, the hardware of the circuit set mayinclude variably connected physical components (e.g., execution units,transistors, simple circuits, etc.) including a computer readable mediumphysically modified (e.g., magnetically, electrically, moveableplacement of invariant massed particles, etc.) to encode instructions ofthe specific operation. In connecting the physical components, theunderlying electrical properties of a hardware constituent are changed,for example, from an insulator to a conductor or vice versa. Theinstructions enable embedded hardware (e.g., the execution units or aloading mechanism) to create members of the circuit set in hardware viathe variable connections to carry out portions of the specific operationwhen in operation. Accordingly, the computer readable medium iscommunicatively coupled to the other components of the circuit setmember when the device is operating. In an example, any of the physicalcomponents may be used in more than one member of more than one circuitset. For example, under operation, execution units may be used in afirst circuit of a first circuit set at one point in time and reused bya second circuit in the first circuit set, or by a third circuit in asecond circuit set at a different time.

The controller circuit 240 may control the operations of the sensorcircuit 210, the signal processor circuit 220, the detector circuit 230,the user interface 250, and the data and instruction flow between thesecomponents. The user interface 250 may include an output unit togenerate a human-perceptible presentation of diagnostic information,such as a display of readmission risk score. The output unit maygenerate an alert if readmission risk score satisfies a specifiedcondition, such as if the difference between the CRI and rCRI exceeds athreshold, or if the readmission risk score is categorized as a “high”risk. The output unit may also display information including thephysiological signals or signal metric trends. The information may bepresented in a table, a chart, a diagram, or any other types of textual,tabular, or graphical presentation formats, for displaying to a systemuser. The presentation of the output information may include audio orother human-perceptible media format. The output unit may provide thereadmission risk score to another process such as to recommend ortitrate a therapy. The user interface 250 may also include input devicesuch as a keyboard, on-screen keyboard, mouse, trackball, touchpad,touch-screen, or other pointing or navigating devices. The input devicemay enable a system user such as a clinician to program the parametersused for sensing the physiological signals, generating trends of signalmetrics, or generating the CRI or the rCRI for estimating readmissionrisk. In an example, at least a portion of the user interface 250 may beimplemented in the external system 125.

In some examples, the chronic disease monitoring system 200 mayadditionally include a therapy circuit 270 configured to deliver atherapy to the patient. The therapy may be triggered by a command signalin response to the readmission risk score satisfying a specifiedcondition. Examples of the therapy may include electrostimulationtherapy delivered to cardiac or pulmonary tissue, heart, a nerve tissue,other target tissues in response to the detection of the targetphysiological event, or drug therapy including delivering drug to atissue or organ. In some examples, therapy circuit 270 may be adjust anexisting therapy based at least on the readmission risk score, such asadjusting a stimulation parameter or drug dosage.

FIG. 3 illustrates generally an example of a portion of a system 300 forgenerating a reference CRI (rCRI) from a patient's physiological signalassociated with a prior medical event. The system portion 300 mayinclude a reference CRI generator 360, which may be an embodiment of thereference CRI generator 260 in FIG. 2 . The reference CRI generator 360may be coupled to the sensor circuit 210 for sensing a physiologicalsignal or generating a trend of a signal metric, and a timing circuit320 for timing different periods associated with prior hospitaladmission for chronic cardiac, pulmonary, or renal diseases. In anexample, the timing circuit 320 may receive input from a user, such asvia the user interface 250, about the beginning, end, or duration of atime period before, during, or after a prior hospital admission.

The reference CRI generator 360 may include a sampling circuit 361 thatmay sample the sensed physiological signal or signal metric trend duringthe specified time period. In an example, the sampled physiologicalsignal or signal metric trend may include a post-discharge periodfollowing the prior hospital admission event. The post-discharge periodmay begin at around the hospital discharge date and last forapproximately 2-5 days. In another example, a delayed post-dischargeperiod may be used, which does not begin until after a post-dischargetransition period when the patient's health status is improved andstabilized. The post-discharge transition period may be provided by asystem user such as via the user interface 250. Alternatively oradditionally, one or more physiological sensors may be used to detectpost-discharge health status improvement and stability. The detection ofthe stable and improved health status may trigger the sampling circuit361 to initiate data sampling in the delayed post-discharge period. Inan example, the sampled physiological signal or signal metric trend mayinclude a pre-admission period preceding the prior hospital admissionevent. The pre-admission period may have a duration of approximately 2-5days and end just prior to hospital admission.

As previously discussed with reference to FIG. 2 , the rCRI may becomputed as a statistical measure of daily (or other periodic)maximum-to-minimum signal intensity difference, a spectral parameterfrom spectral analysis, or an ellipticity attribute in amultidimensional signal space spanned by two or more selectedphysiological signals or signal metrics. For a particular physiologicalsignal or signal metric, one or more rCRI corresponding to differenttime periods may be generated, such as post-discharge rCRI 362, delayedpost-discharge rCRI 363, or pre-admission rCRI 364 as illustrated FIG. 3. During the post-discharge period, the patient may be in recovery froma prior hospitalization for the chronic disease (such as worsening HF).During the delayed post-discharge period, the patient may be in a stateof improved and stabilized health status. The post-discharge rCRI 362and delayed post-discharge rCRI 363 therefore represent chronobiologicalrhythm when the patient is in an improved health status. When thereadmission risk score is computed as a difference between the CRI (suchas determined by the CRI generator 224) and the rCRI (such as either thepost-discharge rCRI 362 or the delayed post-discharge rCRI 363), a lowreadmission risk score may be generated if the CRI substantiallyresembles the rCRI, indicating the presence of a strong chronobiologicalrhythm. During the pre-admission period, the patient health status maybe significantly deteriorated. The pre-admission rCRI 364 may thuscorrespond to worsened chronic disease state. A low readmission riskscore may be generated if the CRI substantially improves from thepre-admission rCRI 364 (such as when the difference between the CRI andthe pre-admission rCRI 364 exceeds a threshold value), indicating thepresence of a strong chronobiological rhythm.

The readmission risk generator 232 may generate the readmission riskscore using a combination of two or more of a comparison between the CRIand post-discharge CRI 362, a comparison between the CRI and the delayedpost-discharge rCRI 363, and a comparison between the CRI and thepre-admission rCRI 364. The combination may include weighted sum orother linear or nonlinear combination.

The reference CRI generator 360 may generate, for each of a plurality ofcandidate physiological signals or signal metrics {X₁, X₂, . . . ,X_(N)}, respective post-discharge rCRI 362, delayed post-discharge rCRI363, or pre-admission rCRI 364. The sensor selector circuit 222 mayselect a physiological signal or signal metric based on a comparison ofthe rCRIs, associated with a particular time period of the priorhospital admission, for all the signals or signal metrics {X₁, X₂, . . ., X_(N)}. In an example, a physiological signal or signal metric X_(k)corresponding to the largest post-discharge rCRI among {X₁, X₂, . . . ,X_(N)} may be selected. Chronobiological rhythm may recover followingpatient discharge, resulting in an increase in post-discharge rCRIvalue. The signal metric X_(k) with the largest post-discharge rCRI ismore sensitive to a post-discharge recovery of chronobiological rhythm,and may therefore be preferred over a signal metric having a smallerpost-discharge rCRI for assessing readmission risk. In another example,the chronobiological rhythm may diminish or become irregular just priorto hospital admission such as due to worsening of the chronic disease. Asignal metric X_(k) having a smaller corresponding pre-admission rCRImay be selected, as it may be more sensitive to pre-admissiondeterioration of chronobiological rhythm than a signal metric having alarger pre-admission rCRI for assessing readmission risk estimationvalue. In another example, the sensor selector circuit 222 may select aphysiological signal or signal metric using a relative change (such as adifference) from a pre-admission rCRI to a post-discharge rCRI. Among{X₁, X₂, . . . , X_(N)}, a signal metric X_(k) with the largest changefrom pre-admission to post-discharge rCRI value may be more sensitive toa recovery process of chronobiological rhythm from pre-admission topost-discharge recovery period, and may therefore be selected forassessing readmission risk.

FIG. 4 illustrates generally an example of a multi-sensor readmissionrisk assessment system 400, which may be an embodiment of the chronicdisease monitoring system 200. The multi-sensor readmission riskassessment system 400 may be configured to determine a patient'sreadmission risk based on the CRIs respectively generated from multiplesensor signals. The multi-sensor readmission risk assessment system 400may include a sensor circuit 410 that includes a plurality of sensorsconfigured to sense respective physiological signals from the patient.By way of non-limiting examples and as illustrated in FIG. 4 , thesensors may include a heart rate sensor 411, a heart sound sensor 412, athoracic impedance sensor 413, a respiration sensor 414, a pressuresensor 415, a physical activity/posture sensor 416, or a chemical sensor417.

The heart rate sensor 411 may detect a heart rate (HR) signal, or astatistical measurement from the heart rate signal, such as a heart ratevariability (HRV). Circadian rhythms (or oscillatory patterns at otherperiods) of HR or HRV, when lost, diminished, or otherwise changed froma reference level such as during a time period associated with a priorhospital admission, may indicate a change of chronic disease status,such as a worsening or improvement of HF status. For example, a healthysubject's HR manifests a daily oscillatory pattern with a lower HRduring sleep than during an awake state. HRV of a healthy subjectgenerally is lower during daytime or an awake state, and increasesduring nighttime or sleep. This circadian rhythms of HR or HRV maybecome less pronounced, more irregular, or otherwise change severalhours to several days before the onset of a disease state, such as aworsening heart failure.

The HS sensor 412 may sense HS information indicative of acoustic ormechanical activity of a heart, which may include S1, S2, S3 or S4 heartsounds. Examples of the HS sensor may include an accelerometer, anacoustic sensor, a microphone, a piezo-based sensor, or othervibrational or acoustic sensors. The HS sensors may be implantable,wearable, holdable, or otherwise ambulatory sensor, and placed externalto the patient or implanted inside the body. The HS sensor may beincluded in at least one part of an ambulatory system, such as the AMD110, or a lead coupled to the ambulatory medical device such as forsensing endocardial acceleration. In a health subject, intensity of HScomponents such as S1, S2, S3, or S4 heart sounds, or cardiac timingintervals, may manifest circadian rhythm or oscillatory patterns atother periods. The intensity of HS component may be measured as peakamplitude of HS component, or signal energy within a time window fordetecting a HS component. The cardiac timing intervals may includepre-ejection period or left ventricular ejection time that may bemeasured from a cardiac event (such as Q or R wave in an ECG signal) toa HS component (such as S1 or S2 heart sounds). This HS circadian rhythmmay be lost, diminished, or otherwise changed from a reference levelsuch as at a time period during a prior hospital admission, a change ofHF status is indicated.

The thoracic impedance sensor 413 may sense thoracic impedance such asby using one or more implanted electrodes on the lead system 108 and thecan housing of the AMD 110. The thoracic impedance may reflect thoracicfluid status. In a healthy subject, distribution of thoracic fluid mayfollow a circadian rhythm. Accordingly, the thoracic impedance may alsomanifest an oscillatory pattern in which the impedance is lower duringnight or sleep at least due to more thoracic fluid accumulation, andhigher during daytime or upright position at least due to less thoracicfluid accumulation. This circadian rhythm of thoracic impedance,however, may begin to shift, become less pronounced, or otherwise changeseveral hours to several days before the onset of a disease state, suchas a worsening heart failure.

The respiration sensor 414 may include an implantable, wearable,holdable, or otherwise ambulatory sensor for sensing a respirationsignal. Examples of the respiration sensor may include an accelerometer,a microphone, an impedance sensor, or a flow sensor. The respiratorysensor 414 may detect one or more respiration parameters such as one ormore of a tidal volume (TV), a respiration rate (RR), a respiration ratevariability (RRV), a minute ventilation (MV), or a rapid-shallowbreathing index (RSBI) computed as a ratio of a respiratory ratemeasurement to a tidal volume measurement, among others. In a healthysubject, respiration parameters such as RR, RRV, MV, or RSBI maymanifest a pronounced, regular circadian rhythm or oscillatory patternat other periods. This circadian rhythm of respiration parameters,however, may become lost, diminish, or otherwise change from severalhours to several days before the onset of a disease state, such as aworsening heart failure. The loss of diminish of the circadian rhythm ofthe respiration parameters may include a decrease in low-frequencycomponent of the RR (as the subject is less likely to be active), and anincrease in high-frequency component of the RR.

The pressure sensor 415 may include an implantable, wearable, holdable,or otherwise ambulatory sensor for sensing a cardiovascular bloodpressure or a pressure within a heart chamber or a surround vascularstructure. In healthy subjects, cardiovascular pressure follows acircadian rhythm. For instance, the blood pressure typically rises inthe morning and stays elevated until late afternoon, at which time itdrops off and hits its lowest point during the night. This circadianrhythm of cardiovascular pressure, however, may become less pronouncedor otherwise change several hours to several days before the onset of adisease state, such as a worsening heart failure. Monitoring thecircadian rhythm of cardiovascular pressure in such instances provides atool to predict, monitor, or treat an occurrence of impending heartfailure.

The physical activity/posture sensor 416 may include an implantable,wearable, holdable, or otherwise ambulatory sensor for sensing anintensity of physical activity or a posture state of the subject. Thephysical activity/posture sensor may include a single-axis or amulti-axis accelerometer configured to sense an acceleration signal ofat least a portion of the subject's body. The strength of theacceleration signal can be indicative of the physical activity level. Inanother example, the activity sensor can include a respiratory sensorconfigured to measure respiratory parameters correlative or indicativeof respiratory exchange, i.e., oxygen uptake and carbon dioxide output.In an example, posture can be represented by, for example, a tilt anglesensed by a tilt switch. In another example, patient posture or physicalactivity information can be derived from thoracic impedance information.In healthy subjects, physical activity and posture may each follow acircadian rhythm. For instance, physical activity intensity is typicallyhigher during the day and reduces at night, and a standing or uprightposture usually occurs during the day and a lying posture occurs atnight. This circadian rhythm of physical activity or posture, however,may become less pronounced or otherwise change several hours to severaldays before the onset of a disease state, such as a worsening heartfailure. Monitoring the circadian rhythm of physical activity or posturein such instances provides a tool to predict, monitor, or treat anoccurrence of impending heart failure.

The chemical sensor 417 may include an implantable, wearable, holdable,or otherwise ambulatory sensor for sensing level or change of bloodchemistry. By way of non-limiting example, the chemical sensor 417 maysense blood electrolyte level such as one or more of potassium (K),sodium (Na) calcium (Ca), glucose, or creatinine. In an example, thechemical sensor 417 may sense a level, or a change of, blood pH. Anexample of an approach to providing a chemical sensor is disclosed inthe commonly assigned Kane et al., U.S. Pat. No. 7,809,441, entitled“IMPLANTABLE MEDICAL DEVICE WITH CHEMICAL SENSOR AND RELATED METHODS,”filed May 17, 2006, which is hereby incorporated by reference in itsentirety, including its disclosure of implantable sensors and sensingmethods associated with changes in the blood electrolytes or pH. Inhealthy subjects. blood chemistry such as levels of one or moreelectrolytes may follow a circadian rhythm. This circadian rhythm may beless pronounced or otherwise change prior to an onset of a diseasestate, such as a worsening heart failure. Monitoring the circadianrhythm of the blood chemistry in such instances provides a tool topredict, monitor, or treat an occurrence of impending heart failure.

The CRI generator 420, which may be an embodiment of the CRI generator224, may receive the sensor signals sensed by various sensors in thesensor circuit 410, and generate respective CRIs from the sensorsignals. The reference CRI generator 260 may generate, for the varioussensor signals, respective rCRI associated with a prior hospitaladmission event. In an example, as previously discussed with referenceto FIG. 3 , the rCRI may be generated during a specified time periodsuch as a pre-admission period, a post-discharge period, or a delayedpost-discharge period associated with prior hospital admission.

The readmission risk generator 430, which may be an embodiment of thereadmission risk generator 232, may include a comparator circuit 432 tocompare the CRIs with the corresponding rCRIs, and a fusion circuit 434to determine a readmission risk using a combination of the comparisonsbetween the CRIs and the corresponding rCRIs. In an example, thecomparator circuit 432 may compute pair-wise difference or othersimilarly measure between the CRI and the corresponding rCRI for each ofthe sensor signals or of a subset of the sensor signals such as selectedby the sensor selector circuit 222, and the fusion circuit 434 maydetermine the readmission risk score (R) as weighted combination, orother linear or nonlinear combination, of the pair-wise differences forall or the selected subset of the sensor signals, such as according toEquation (1) below:

R=Σw(i)·[CRI(i)−rCRI(i)]  (1)

where w(i) is a weight factor for sensor signal X₁. In another example,the readmission risk generator 430 may compute a composite CRI as aweighted combination of the CRIs for all or a selected subset of thesensor signals, and compute a composite rCRI as a weighted combinationof the rCRIs for all or a selected subset of the sensor signals. Thereadmission risk may be determined as a difference, ratio, or similarlymeasure between the composite CRI and the composite rCRI, such asaccording to Equation (2) below:

R=Σu(i)·CRI(i)−Σv(j)·rCRI(j)  (2)

where u(i) is a weight factor for CRI(i) and v(j) is a weight factor forrCRI(j).

In some examples, the readmission risk generator 430 may determine thereadmission risk score using time elapsed (ΔT) from the prior hospitaladmission event. A more recent hospital admission event (correspondingto a smaller ΔT) may put the patient at a higher readmission risk than amore remote hospital admission in the past (corresponding to a largerΔT). In an example, the weight factors, such as the weight factors w, uor v in Equations (1) and (2) for computing the readmission risk R, maybe determined based on the elapsed time ΔT. In an example, the rCRIs arecomputed using signals or signal metric trends in a post-dischargeperiod. The weight factors w, u or v may be inversely proportional tothe elapsed time ΔT. This may be useful when different sensor signalsare measured at different time. For example, if CRI(i) is measured fromsensor signal X(i) at a time closer to the prior hospital admissionevent (i.e., a smaller ΔT), while CRI(j) is measured from sensor signalX(j) at a time more distant away from the prior hospital admission event(i.e., a larger ΔT), then in Equation (1), the weight w(i) fordifference [CRI(i)−rCRI(i)] would be larger than the weight factor w(j)for the difference [CRI(j)−rCRI(j)].

FIGS. 5A-B illustrate generally examples of an ellipticity analyzercircuit for determining a chronobiological rhythm indicator using aplurality of physiological signals or signal metric trends. Theellipticity analyzer circuit 510 in FIG. 5A and the ellipticity analyzercircuit 520 in FIG. 5B may each be an embodiment of the CRI generator224 for generating the CRI from a physiological signal or signal metrictrend, or an embodiment of the reference CRI generator 260 forgenerating the rCRI associated with a prior hospital admission event.

As illustrated in FIG. 5A, the ellipticity analyzer circuit 510 mayinclude a multidimensional signal representation circuit 512 forreceiving two or more physiological signals or signal metric trends fromthe sensor circuit 210, and representing the signals or signal metrictrends in a multidimensional signal space. In an example, themultidimensional signal space may be spanned by a heart rate in a firstaxis, a HS metric such as S1 intensity in a second axis, and a thoracicimpedance magnitude in a third axis. Multiple measurements of heartrate, S1 intensity and thoracic impedance magnitude may be representedas a cloud of data points in the multidimensional signal space, whereeach data point represents the heart rate, the S1 intensity, and thethoracic impedance magnitude measured substantially simultaneously, suchas during the same cardiac cycle or within the same data acquisitionwindow.

The covariation pattern generator 514 may determine an ellipticityattribute from the multidimensional data representation. In an example,the covariation pattern generator 514 may generate a multidimensionalellipse based on the statistical distribution of the multidimensionaldata. The multidimensional ellipse is a graphical representation of thecovariation pattern among multiple signal metrics. If multiple signalmetrics strongly covariate with one another, then there is a highlikelihood of an underlying chronobiological rhythm such that differentsensor signals or signal metrics represent similar physiology. Themultidimensional data representation with strong covariation maygraphically manifest in the multidimensional signal space more of anellipse than a circle. If multiple signal metrics weakly covariate withone another, then there is a low likelihood of an underlyingchronobiological rhythm. The multiple signal metrics may representdissimilar physiology. The multidimensional data representation withweak covariation may graphically manifest in the multidimensional signalspace less of an ellipse, but more of a circle.

The readmission risk generator 232 may compare the CRI represented asthe covariation pattern (CP) among multiple signals or signal metrics tothe rCRI represented as the reference covariation pattern (rCP) amongmultiple signals or signal metrics associated with a prior hospitaladmission event. The readmission risk generator 232 may determine thereadmission risk using a similarity measure between CP and rCP. Examplesof the similarity measure may include Euclidian distance, correlationcoefficient, or mutual information, among others. In an example, the rCPis determined using signals or signal metrics during a post-dischargeperiod when the patient is recovered from hospitalization for a chronicdisease. The rCP may have a graphical pattern more of an ellipse. If theCP has a graphical pattern of an ellipse and is substantially similar torCP (such as if the similarity measure exceeds a threshold), then a highchronobiological rhythm is indicated, and a low readmission score may begenerated. If the CP has a graphical pattern more of a circle than anellipse, such that it is substantially dissimilar to rCP (such as if thesimilarity measure falls below a threshold), then it indicates thechronobiological rhythm has been lost or substantially diminished, andthe patient's chronic disease state deteriorates and a high readmissionrisk score may be determined.

As illustrated in FIG. 5B, the ellipticity analyzer circuit 520 mayinclude the multidimensional data representation circuit 512 acovariance matrix calculator 522, a principal component analyzer circuit524, and one or both of an eigenvalue dominance analyzer 526 and aprojection circuit 528. The covariance matrix calculator 522 may receivetwo or more physiological signals or signal metrics and compute acovariance matrix. For N signals or signal metrics, the covariancematrix would be an N-by-N matrix. The principal component analyzercircuit 524 performs principal component analysis (PCA), such as byusing a Karhunen Loeve Transform (KLT), on the covariance matrix. ThePCA analysis may result in a plurality of principal components andcorresponding eigenvalues for the principal components. The principalcomponents are orthogonal dimensions or uncorrelated directions in themultidimensional signal space, and can be viewed as a subset of allpossible eigenvectors. For a N-by-N covariance matrix, up to N principalcomponents (e.g., V₁, V₂, . . . , V_(N)) and corresponding N eigenvalues(e.g., λ₁, λ₂, . . . ) can be obtained. In some examples, the principalcomponents may be sorted (e.g., in descending order) according to thevariance of the projections of the multidimensional data along theprincipal components. The first principal component may have as high avariance as possible. Each succeeding principal component has the nexthighest variance possible and is constrained to be orthogonal to thepreceding principal components.

In an example where two signals or signal metrics, X and Y, areinvolved, the ellipticity analyzer circuit 520 may generate a 2-by-2matrix:

${C{xy}} = \begin{bmatrix}\sigma_{x}^{2} & \rho_{xy} \\\rho_{xy} & \sigma_{y}^{2}\end{bmatrix}$

where σ_(x) ² is the variance of the first signal X, σ_(y) ² is thevariance of the second signal Y, and ρ_(xy) is the covariation betweenthe signals X and Y. By applying the KLT, the covariance matrix Cxy maybe transformed into a diagonal matrix of eigenvalues λ₁ and λ₂:

${Cxy}^{\prime} = \begin{bmatrix}\lambda_{1} & 0 \\0 & \lambda_{2}\end{bmatrix}$

By way of example and not by limitation, the eigenvalue dominanceanalyzer 526 and the projection circuit 528 provide two approaches forassessing readmission risk, either or both of which may be included inthe ellipticity analyzer circuit 520. The eigenvalue dominance analyzer526 may determine a relative measure among the plurality of eigenvalues.The relative measure may indicate dominance of the maximum eigenvalue(λ_(max)) among all the eigenvalues. A more dominant λ_(max) mayindicate a higher ellipticity of the multidimensional datarepresentation in the multidimensional signal space, or a graphicalpattern more of an ellipse than a circle. Conversely, a less dominantλ_(max) may indicate a lower ellipticity of the multidimensional datarepresentation, or a graphical pattern more of a circle than an ellipse.In an example, the relative measure may be calculated using a ratio ofλ_(max) to a sum of all the eigenvalues Σλ_(i), as provided in Equation(3) below:

$\begin{matrix}{L = \frac{\lambda_{\max}}{\Sigma\lambda_{i}}} & (3)\end{matrix}$

Alternatively, the ellipticity L may be determined based on astatistical distribution of the eigenvalues, such as a range,spreadness, skewness, variance of the eigenvalues. In an example, theellipticity may be determined as a difference between λ_(max) and theminimum eigenvalue λ_(min). A greater difference (λ_(max)-λ_(min)) mayindicate greater dominance of λ_(max) and thus higher ellipticity. Inanother example, the ellipticity may be determined as a different, or aratio, between λ_(max) and a mean, median, mode or other centraltendency measure of all the eigenvalues. A greater ratio of λ_(max) andthe central tendency of eigenvalues, or a greater difference betweenλ_(max) and the central tendency of eigenvalues, may indicate greaterdominance of λ_(max) and thus higher ellipticity.

The readmission risk generator 232 may determine the readmission riskbased on a relative change of the ellipticity (L) such as given inEquation (3) above from the reference ellipticity (rL) associated with aprior hospital admission event. In an example, the rL may be determinedusing two or more signals or signal metrics acquired during apost-discharge period when the patient is recovered from hospitalizationfor a chronic disease. If the ellipticity L is greater than orsubstantially equal to the reference ellipticity attribute rL, then ahigh chronobiological rhythm is indicated, and a low readmission scoremay be generated. However, if the ellipticity L falls below thereference ellipticity attribute rL by a specified margin, then itindicates the chronobiological rhythm has been lost or substantiallydiminished, the chronic disease state has deteriorated, and a highreadmission risk score may be generated.

The projection circuit 528 may generate a projection of themultidimensional data received from the sensor circuit 210 along atleast one of the principal components. The projection is a transformthat reduces the dimension of the multidimensional data to a dimensiondetermined by the principal components. The multidimensional data may berepresented by a data matrix, and each principal component representedas a vector. The projection may involve matrix-matrix multiplication ormatrix-vector multiplication. In an example, the projection circuit 528may project the multidimensional data along one principal component suchas corresponding to λ_(max), resulting in one-dimensional transformeddata. In another example, the projection circuit 528 may project themultidimensional data along two principal components such ascorresponding the maximal and second maximal eigenvalues, resulting in atwo-dimensional transformed data.

In an example, the ellipticity analyzer circuit 520 may be an embodimentof the reference CRI generator 260, and configured to generateellipticity attributes associated with a prior hospital admission event.The covariance matrix calculator may compute a reference covariancematrix using a plurality of physiological signals or signal metrictrends during respective time periods (such as a pre-admission period ora post-discharge period) associated with the prior hospital admissionevent. The principal component analyzer circuit 524 may determine two ormore reference principal components from the reference covariancematrix. At least one of the reference principal components may be usedfor data transformation. The projection circuit 528 may determine afirst projection of the multidimensional data along at least one of thereference principal components. The first projection representstransformed data from which a readmission risk is to be assessed. Theprojection circuit 528 may determine a second projection of amultidimensional data associated with the prior hospital admission eventalong the at least one of the reference principal components. The secondprojection represents transformed data associated with prior hospitaladmission. A CRI may be generated from first projection, and a referenceCRI (rCRI) may be generated from the second projection. The CRI (orrCRI) may be calculated as a statistical measure of daily (or otherperiodic) maximum-to-minimum intensity difference of a respectiveprojection, or a spectral parameter obtained from the spectral analysisof the respective projection. The readmission risk generator 232 maydetermine the readmission risk score including a relative change fromrCRI to CRI.

FIG. 6 illustrates generally an example of a method 600 for monitoring achronic disease in a patient, such as a chronic heart disease such asheart failure or coronary artery disease, a chronic pulmonary diseasesuch as asthma, bronchoconstriction, COPD, or pulmonary fibrosis,pneumoconiosis, or a chronic kidney disease, among others. The method600 may be used to monitor the patient's chronic disease state followingpatient's discharge from the hospital, and to determine a risk ofhospital readmission for worsening of chronic disease. The method 600may be implemented and operate in an ambulatory medical device such asan implantable, wearable, or holdable medical device, or in a remotepatient management system. In an example, the method 500 may be executedby the readmission risk analyzer module 160 or by the external system125. In an example, the method 600 may be implemented in, and executedby, the chronic disease monitoring system 200 or any embodimentsthereof.

The method 600 begins at 610 by sensing one or more physiologicalsignals such as via implantable, wearable, holdable, or otherwiseambulatory sensors, which may include, by way of example and notlimitation, pressure sensors, flow sensors, impedance sensors,accelerometers, microphone sensors, respiration sensors, temperaturesensors, or chemical sensors, among others. Examples of thephysiological signals sensed by the physiological sensor circuit 210 mayinclude electrocardiograph (ECG), an electrogram (EGM), an intrathoracicimpedance signal, an intracardiac impedance signal, an arterial pressuresignal, a pulmonary artery pressure signal, a RV pressure signal, a LVcoronary pressure signal, a coronary blood temperature signal, a bloodoxygen saturation signal, a blood chemistry signal such as a bloodelectrolyte level signal, glucose level signal or creatinine levelsignal, central venous pH value, a heart sound (HS) signal, anendocardial acceleration signal, an angular momentum signal, a posturesignal, a physical activity signal, or a respiration signal, amongothers. One or more signal metrics may be generated from the sensedphysiological signals, and trended over time to form respective signalmetric trends.

At 620 a chronobiological rhythm indicator (CRI) may be generated fromthe sensed physiological signals. The CRI may be determined usingmultiple measurements of daily (or other periodic) maximum-to-minimumintensity difference of signal X (denoted by X_(pp)) over a specifiedtime period, such as approximately 5-10 days. Alternatively, the CRI mayinclude one or more spectral parameters obtained from a spectralanalysis of the selected physiological signals or signal metric trends,such as power of a spectral peak corresponding to the circadian rhythm,a center frequency of the spectral peak, or a bandwidth of the spectralpeak. In some examples, the CRI may be an ellipticity attributerepresented in a multidimensional signal space spanned by two or moreselected physiological signals or signal metrics, as to be discussedbelow with reference to FIG. 7 .

At 630, a reference CRI (rCRI) may be provided by a system user, orgenerated from physiological signals or signal metric trends over aspecified time period such as associated with prior hospital admission.In an example, the rCRI may be determined from physiological signals orsignal metric trends during a post-discharge period of approximately 2-5days following the prior hospital admission event. In another example,the rCRI may be determined from physiological signals or signal metrictrends during a delayed post-discharge period that does not begin untilafter a post-discharge transition when the patient's health status isimproved and stabilized. In an example, the rCRI may be determined fromphysiological signals or signal metric trends during a pre-admissionperiod of approximately 2-5 days preceding the prior hospital admissionevent.

At 640, a readmission risk score may be generated using the CRI and therCRI. The readmission risk score may indicate a degree of risk ofsubsequent hospital readmission due to a worsened condition of thechronic disease. The readmission risk score may be determined as adifference, ratio, or other relative measure between CRI and rCRI, or asimilarity measure between an ellipticity attribute and a referenceellipticity attribute based on ellipticity analysis of multidimensionaldata of the physiological signals or signal metrics, as to be discussedbelow with reference to FIG. 7 . The readmission risk score may takecontinuous values. The readmission risk score may be compared to one ormore threshold values or ranges, and categorized into discretecategorical levels such as high, medium, or low risk of readmission.

In an example, the readmission risk score may be calculated using arelative change of CRI from a post-discharge rCRI or a delayedpost-discharge rCRI. The post-discharge rCRI or the delayedpost-discharge rCRI may represent a chronobiological rhythm indicatingimproved health status. If the difference between CRI and post-dischargerCRI or delayed post-discharge rCRI falls below a threshold value, thena strong chronobiological rhythms is indicated, and a low readmissionrisk score is generated. In another example, the readmission risk scoremay be calculated using a relative change of CRI from a pre-admissionrCRI. The pre-admission rCRI may correspond to worsened chronic diseasestate prior to hospital admission. If the difference between the CRI andthe pre-admission rCRI exceeds a threshold value, then a strongchronobiological rhythm is indicated, and a low readmission risk scoreis generated. In an example, the readmission risk score may bedetermined using a weighted combination of a difference between CRI andthe pre-admission rCRI and a difference between the CRI and thepost-discharge CRI or the delayed post-discharge rCRI.

In various examples, the CRI at 620 may be generated from a subset ofthe plurality of candidate physiological signals or signal metricsselected based on the physiological signal or signal metric'ssensitivity to patient chronobiological rhythms. Generally, a signal orsignal metric that manifests a higher level of daily, weekly, monthly,or seasonal oscillatory pattern may be more sensitive tochronobiological rhythms than a signal or signal metric showing no orlower level of chronological oscillatory pattern, and may thus beselected for generating the CRI. In an example, the subset ofphysiological signals or signal metrics may be selected based on thereference CRIs (rCRIs) associated with a particular time period of theprior hospital admission. For example, a signal metric having a largerpost-discharge rCRI or a smaller pre-admission rCRI, has a highersensitivity to a post-discharge recovery of chronobiological rhythm, andmay therefore be preferred over a signal metric having a smallerpost-discharge rCRI or a larger pre-admission rCRI. In another example,a signal or signal metric having a larger change from pre-admission topost-discharge rCRI value is more sensitive to a recovery process ofchronobiological rhythm from pre-admission to post-discharge recoveryperiod, and may therefore be selected for use in readmission riskestimation.

In some examples, the readmission risk score may be determined usingchronobiological rhythm indicators (CRIs) generated from multiplephysiological signals or signal metrics. Examples of the physiologicalsignals may include heart rate signal, heart rate variability signal,heart sound signal, endocardial acceleration signal, angular momentumsignal, thoracic impedance signal, respiration signal, pressure signalsuch as cardiovascular blood pressure signal or thoracic pressuresignal, physical activity intensity signal, or posture signal. Circadianrhythms (or oscillatory patterns at other periods) of one or more ofthese physiological signals, when lost, diminished, or otherwise changedfrom a reference level such as at a time period during a prior hospitaladmission, may be associated with a change of chronic disease status.CRIs for the multiple signals or signal metrics may be generated at 620,and the corresponding rCRIs for the multiple signals or signal metricsmay be generated at 630. A readmission risk score may be generated at640, such as via the fusion circuit 434, using a weighted combination ofthe difference, ratio, or similarly measure between the CRI and thecorresponding rCRI for all or a selected subset of the physiologicalsignals.

At 650, the readmission risk score may be provided to a user or aprocess. At 552, a human-perceptible presentation of the readmissionrisk score, or the CRI and the rCRI, may be generated, and displayedsuch as on the user interface 250. The information may be presented in atable, a chart, a diagram, or any other types of textual, tabular, orgraphical presentation formats. An alert may be generated in response tothe respiratory restriction/obstruction indicator satisfying a specifiedcondition, such as exceeding an alert threshold. Additionally oralternately, at 654, the readmission risk score may be used to triggeror adjust a therapy delivered to the patient. Examples of the therapymay include electrostimulation therapy delivered to the heart, a nervetissue, other target tissues in response to the detection of the targetphysiological event, or drug therapy including delivering drug to atissue or organ. In some examples, the detection or the classificationof the restrictive or obstructive respiratory condition may be used tomodify an existing therapy, such as to adjust a stimulation parameter ordrug dosage.

FIG. 7 illustrates generally an example of a method 700 for determininga chronobiological rhythm indicator based on ellipticity analysis of atleast two physiological signals or signal metric trends. The method 700may be executed by the ellipticity analyzer circuits 510 or 520 asillustrated in FIGS. 5A-B, or any embodiments thereof.

The method 700 may include steps 712-718 for determining a referenceellipticity attribute, and steps 722-728 for determining an ellipticityattribute. At 712, at least two physiological signals or signal metricmay be sensed during a time period associated with a specified event inthe patient's medical history, such as a pre-admission period or apost-discharge period associated with a prior hospital admission event.At 714, a multidimensional data representation of the two or moresignals or signal metrics may be generated in a multidimensional signalspace. Each dimension (or axis) of the multidimensional signal space maybe represented by a physiological signal or signal metric. For example,the multidimensional signal space may be spanned by a heart rate in afirst axis, a S1 heart sound intensity in a second axis, and a thoracicimpedance magnitude in a third axis. A multidimensional datarepresentation may be represent as a cloud of data points in themultidimensional signal space, including multiple measurements of theheart rate, the S1 intensity, and the thoracic impedance magnitudeduring the prior hospital admission event.

At 716, a covariance matrix may be generated using the multidimensionaldata representation, and a principal component analysis (PCA) may beperformed on the covariance matrix. As previously discussed withreference to FIG. 5B, the PCA may include a Karhunen Loeve Transform(KLT) of the covariance matrix that produces a plurality of referenceprincipal components and corresponding reference eigenvalues for thereference principal components. The reference principal components areorthogonal dimensions or uncorrelated directions in the multidimensionalsignal space.

At 718, a reference ellipticity attribute may be determined using theeigenvalues or the principal components. In an example, the referenceellipticity attribute (rL) may include a measure of dominance of themaximum reference eigenvalue (λ_(max)) among all the referenceeigenvalues, such as determined according to Equation (3). A moredominant λ_(max) may indicate a higher ellipticity of themultidimensional data representation in the multidimensional signalspace, or a graphical pattern more of ellipse than circle. Conversely, aless dominant λ_(max) may indicate a lower ellipticity of themultidimensional data representation, or a graphical pattern more ofcircle than ellipse. In another example, the reference ellipticityattribute (rL) may include a projection of the multidimensional data, asgenerated at 714, along one or more reference principal components. Theprojection may be performed using matrix-matrix multiplication ormatrix-vector multiplication, where the multidimensional data isrepresented by a matrix and each reference principal component may berepresented as a vector. The projection represents transformed dataassociated with the prior hospital admission event.

Similar to steps 712-718, an ellipticity attribute may be generated fromat least two physiological signals or signal metrics. In contrast to 712where the signals or signal trends are sensed during a time periodassociated with prior hospital admission, at 722 the signals or signaltrends may be sensed during a time period subsequent to the priorhospital admission and an assessment of patient's risk of readmission isneeded. A multidimensional data representation of the two or morephysiological signals or signal metric trends may be generated at 724,and at 726 PCA may be performed on a covariance matrix generated fromthe multidimensional data representation. An ellipticity attribute maybe generated at 728, which may include a dominance measure of themaximum eigenvalue λ_(max) among all the eigenvalues obtained from PCAof the covariance matrix, such as determined according to Equation (3).

In an example, the ellipticity attribute at 728 may include a projectionof the multidimensional data, as generated at 724, along one or morereference principal components generated at 716 through PCA of thecovariance matrix of the multidimensional data associated with the priorhospital admission event. If the ellipticity attribute is representedonly by the projection onto one or more reference principal components,the step 726 may be omitted. The projection represents transformed datafrom which the patient's readmission risk is to be assessed.

At 730, a readmission risk score may be generated using the referenceellipticity attribute and the ellipticity attribute. In an example, thereference ellipticity attribute includes a dominance of the maximumreference eigenvalue, which is computed from signals acquired during apost-discharge period when the patient is recovered from hospitalizationfor a chronic disease. The ellipticity attribute includes a dominance ofthe maximum eigenvalue. If the ellipticity (L) is greater than orsubstantially equal to the reference ellipticity attribute (rL), then ahigh chronobiological rhythm is indicated, and a low readmission scoremay be generated. However, if the ellipticity L falls below thereference ellipticity attribute rL by a specified margin, then itindicates the chronobiological rhythm has lost or substantiallydiminished, and the patient's chronic disease state deteriorates and ahigh readmission risk score is determined.

In another example, the readmission risk score may be determined at 730using a first projection of the multidimensional data generated at 724along one or more reference principal components, and a secondprojection of the multidimensional data generated at 714 along the sameone or more reference principal components. A CRI may be generated fromfirst projection, such as a statistical measure of daily (or otherperiodic) maximum-to-minimum intensity difference of the firstprojection, or spectral parameters obtained from the spectral analysisof the first projection. A reference CRI (rCRI) may be generated fromthe second projection, such as a statistical measure of daily (or otherperiodic) maximum-to-minimum intensity difference of the secondprojection, or spectral parameters obtained from the spectral analysisof the second projection. The readmission risk generator may then becalculated using a relative change of the CRI from the rCRI.

In some examples, the ellipticity attribute at 718 may include areference covariance pattern (rCP) generated from the multidimensionaldata representation at 714, and the ellipticity attribute at 728 mayinclude a covariance pattern (CP) generated from the multidimensionaldata representation at 724. The rCP and CP may each have a graphicalrepresentation in the multidimensional signal space. Themultidimensional data representation with strong covariation amongsignal metrics may have a graphical pattern more of an ellipse than acircle, while the multidimensional data representation with weakcovariation may have a graphical pattern more of a circle than anellipse. At 730, the readmission risk score may be determined based on asimilarity measure between the rCP and CP. Examples of the similaritymeasure may include Euclidian distance, correlation coefficient, ormutual information, among others. If the similarity measure exceeds athreshold, then a high chronobiological rhythm is indicated, and a lowreadmission score may be generated. If the similarity measure fallsbelow a threshold, then it indicates the chronobiological rhythm hasbeen lost or substantially diminished, and the patient's chronic diseasestate deteriorates and a high readmission risk score may be determined.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which thedisclosure may be practiced. These embodiments are also referred toherein as “examples.” Such examples may include elements in addition tothose shown or described. However, the present inventors alsocontemplate examples in which only those elements shown or described areprovided. Moreover, the present inventors also contemplate examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

In the event of inconsistent usages between this document and anydocuments so incorporated by reference, the usage in this documentcontrols.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

Method examples described herein may be machine or computer-implementedat least in part. Some examples may include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods may include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code may include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code may be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. § 1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. Also, in the above Detailed Description,various features may be grouped together to streamline the disclosure.This should not be interpreted as intending that an unclaimed disclosedfeature is essential to any claim. Rather, inventive subject matter maylie in less than all features of a particular disclosed embodiment.Thus, the following claims are hereby incorporated into the DetailedDescription as examples or embodiments, with each claim standing on itsown as a separate embodiment, and it is contemplated that suchembodiments may be combined with each other in various combinations orpermutations. The scope of the disclosure should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A medical-device system for monitoring heartfailure in a patient, the medical-device system comprising: a receivercircuit configured to receive physiological information sensed from thepatient using a plurality of sensors; and a heart failure event detectorcircuit configured to: determine a reference chronobiological rhythmindicator (CRI) using a first portion of the received physiologicalinformation sensed during a first time period after a post-dischargetransition period; generate a patient CRI using a second portion of thereceived physiological information sensed during a second time perioddifferent than the first time period; and detect a heart failure statusof the patient using the generated patient CRI and the reference CRI. 2.The medical-device system of claim 1, wherein the received physiologicalinformation includes cardiac electrophysiological information.
 3. Themedical-device system of claim 2, wherein the cardiacelectrophysiological information includes one or more of a heart rate, aheart rate variability, a cardiac timing parameter, or a cardiacarrhythmia event.
 4. The medical-device system of claim 1, wherein thereceived physiological information includes information about therapiesdelivered to the patient and patient physiological responses thereto. 5.The medical-device system of claim 4, comprising an ambulatory medicaldevice configured to deliver the therapies including cardiacelectrostimulation.
 6. The medical-device system of claim 1, wherein theheart failure event detector circuit is configured to detect the heartfailure status of the patient based at least in part on a differencebetween the generated patient CRI and the reference CRI.
 7. Themedical-device system of claim 1, wherein the heart failure eventdetector circuit is configured to detect the heart failure status of thepatient based at least in part on a ratio of the generated patient CRIto the reference CRI.
 8. The medical-device system of claim 1, whereinthe reference CRI is determined using a weighted combination of aplurality of sensor-specific reference CRIs each calculated usingrespective signals sensed by the plurality of sensors during the firsttime period, wherein the patient CRI is determined using a weightedcombination of a plurality of sensor-specific CRIs each calculated usingrespective signals sensed by the plurality of sensors during the secondtime period.
 9. The medical-device system of claim 1, wherein thereference CRI incudes a plurality of sensor-specific reference CRIs eachcalculated using respective signals sensed by the plurality of sensorsduring the first time period, wherein the patient CRI includes aplurality of sensor-specific CRIs each calculated using respectivesignals sensed by the plurality of sensors during the second timeperiod, wherein to detect the heart failure status, the heart failureevent detector circuit is configured to determine a composite risk scoreusing a weighted combination of differences between the plurality ofsensor-specific CRIs and the plurality of sensor-specific referenceCRIs, and to detect the heart failure status based on the composite riskscore.
 10. The medical-device system of claim 9, wherein the heartfailure event detector circuit is configured to determine the compositerisk score further using an elapsed time from a prior hospital admissionevent to a time at which an sensor-specific CRI is calculated during thesecond time period.
 11. The medical-device system of claim 10, whereinthe heart failure event detector circuit is configured to, for each ofthe differences between the plurality of sensor-specific CRIs and theplurality of sensor-specific reference CRIs, determine a weight factorinversely proportional to the elapsed time.
 12. The medical-devicesystem of claim 9, comprising a sensor selector circuit configured toselect a sensor from the plurality of sensors based on thesensor-specific reference CRIs; wherein the heart failure event detectorcircuit is configured to generate the patient CRI using physiologicinformation sensed by the selected sensor.
 13. A method of monitoringheart failure in a patient, comprising: receiving physiologicalinformation of the patient; determining a reference chronobiologicalrhythm indicator (CRI) using a first portion of the receivedphysiological information sensed during a first time period after apost-discharge transition period; generating a patient CRI using asecond portion of the received physiological information sensed during asecond time period different than the first time period; and detecting aheart failure status of the patient using the generated patient CRI andthe reference CRI.
 14. The method of claim 13, wherein the receivedphysiological information includes cardiac electrophysiologicalinformation including one or more of a heart rate, a heart ratevariability, a cardiac timing parameter, or a cardiac arrhythmia event.15. The method of claim 13, wherein the received physiologicalinformation includes information about cardiac electrostimulationdelivered to the patient and patient physiological responses thereto.16. The method of claim 13, wherein detecting the heart failure statusof the patient is based at least in part on a difference between thegenerated patient CRI and the reference CRI.
 17. The method of claim 13,wherein detecting the heart failure status of the patient is based atleast in part on a ratio of the generated patient CRI to the referenceCRI.
 18. The method of claim 13, wherein determining the reference CRIincludes using a weighted combination of a plurality of sensor-specificreference CRIs each calculated using respective signals sensed by theplurality of sensors during the first time period, wherein generatingthe patient CRI includes using a weighted combination of a plurality ofsensor-specific CRIs each calculated using respective signals sensed bythe plurality of sensors during the second time period.
 19. The methodof claim 13, comprising: determining a plurality of sensor-specificreference CRIs each using respective signals sensed by the plurality ofsensors during the first time period; generating a plurality ofsensor-specific CRIs each using respective signals sensed by theplurality of sensors during the second time period; and determining acomposite risk score using a weighted combination of differences betweenthe plurality of sensor-specific CRIs and the plurality ofsensor-specific reference CRIs, wherein detecting the heart failurestatus includes is based at least on the determined composite riskscore.
 20. The method of claim 19, wherein determining the compositerisk score includes, for each of the differences between the pluralityof sensor-specific CRIs and the plurality of sensor-specific referenceCRIs, determining a weight factor inversely proportional an elapsed timefrom a prior hospital admission event to a time at which a correspondingsensor-specific CRI is calculated during the second time period.